Fundamentals of Statistics

Karene Chu serves as the Assistant Director of Education and Research Scientist at MIT's Institute for Data, Systems, and Society, where she has made significant contributions to digital learning initiatives. After receiving her Ph.D. in mathematics from the University of Toronto in 2012, she completed postdoctoral fellowships at both the University of Toronto/Fields Institute and MIT, specializing in knot theory and quantum invariants. In 2015, she transitioned to become a digital learning lab fellow at MIT, where she has since played a pivotal role in developing and managing the MicroMasters Program in Statistics and Data Science. Her educational impact includes co-authoring the MITx Calculus Series, which became a Top 10 edX course in 2016, and leading the development of a five-course series on differential equations. She is also a key instructor in MIT's Machine Learning with Python course alongside Regina Barzilay and Tommi Jaakkola. Her teaching excellence was first recognized at the University of Toronto, where she received a teaching award for her work in single and multi-variable calculus and linear algebra. As part of MIT's edX group, she collaborated with colleagues to earn the inaugural MITx Prize for Teaching and Learning in MOOCs, demonstrating her commitment to advancing digital education and making complex mathematical concepts accessible to learners worldwide.

Jan-Christian Hütter has evolved from an MIT Mathematics graduate student to a leading figure in machine learning and statistical research. After completing his Ph.D. at MIT under Philippe Rigollet's supervision in 2019, he served as a postdoctoral researcher in the Regev group at the Broad Institute, where he was co-advised by Caroline Uhler. Currently, he works as a Principal ML Scientist II in the Biology Research | AI Development (BRAID) department at Genentech, where he develops methods for analyzing omics data and large-scale high-content perturbation screens

Philippe Rigollet currently serves as the Cecil and Ida Green Distinguished Professor of Mathematics at MIT, where he has made significant contributions at the intersection of statistics, machine learning, and optimization. His academic journey began in France, where he earned his B.Sc. in statistics in 2001 and applied mathematics in 2002, followed by a Ph.D. in mathematical statistics in 2006 from the University of Paris VI (now Sorbonne University). Initially labeled a "slacker" in high school, Rigollet found his passion in mathematics, drawn to its inherent freedom and creativity. After completing his doctorate, he held positions at Georgia Tech as a postdoctoral fellow and at Princeton University as an assistant professor in the Department of Operations Research and Financial Engineering before joining MIT in 2015. His research spans high-dimensional statistics, optimal transport, and the mathematical foundations of Transformers, with recent focus on statistical optimal transport and its applications to geometric data analysis and sampling. His groundbreaking work includes establishing theoretical limits for efficiently computing sparse dimensions in high-dimensional datasets and developing statistical methods for clinical trials and web advertising optimization. His exceptional contributions have earned him numerous accolades, including an NSF CAREER award, the Howard B. Wentz Jr. Junior Faculty Award, and the COLT 2013 Best Paper Award. Beyond research, he actively contributes to academic publishing as an associate editor for several prestigious journals and has served as Program Committee co-Chair for COLT 2018.